Load Packages:
library(tidyverse)
library(plotly)
library(janitor)
library(RColorBrewer)
library(factoextra)
library(dendextend)
library(NbClust)
library(cluster)
library(ggdendro)
library(pdftools)
library(tidytext)
library(wordcloud)
iris_nice <- iris %>%
clean_names()
ggplot(iris_nice) +
geom_point(aes(x = petal_length, y = petal_width, color = species))
How many clusters do YOU think should exist, R?
# Use NbClust() to determine the best number of clusters (uses 30 algorithims to determine cluster numbers)
# Give it a minimum and maximum number of clusters to consider and a method (we are using kmeans for clustering)
number_est <- NbClust(iris_nice[1:4], min.nc = 2, max.nc = 10, method = "kmeans")
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 10 proposed 2 as the best number of clusters
## * 8 proposed 3 as the best number of clusters
## * 2 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 1 proposed 7 as the best number of clusters
## * 1 proposed 8 as the best number of clusters
## * 1 proposed 10 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 2
##
##
## *******************************************************************
# Since we have 3 species and almost as many algorithims suggest 3 as 2 we'll stick with three clusters
Performing k-means clustering with 3 groups:
iris_km <- kmeans(iris_nice[1:4], 3)
# How many observations in each cluster?
iris_km$size
## [1] 62 38 50
# What observations are associated with each cluster?
iris_km$centers
## sepal_length sepal_width petal_length petal_width
## 1 5.901613 2.748387 4.393548 1.433871
## 2 6.850000 3.073684 5.742105 2.071053
## 3 5.006000 3.428000 1.462000 0.246000
# What cluster has each observation been assigned to?
iris_km$cluster
## [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [36] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2
## [106] 2 1 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2 1 2 2 2 2 1 2
## [141] 2 2 1 2 2 2 1 2 2 1
# Bind the cluster assignment to the original data
iris_cl <- data.frame(iris_nice, cluster_no = factor(iris_km$cluster))
# Basic ggplots for visualization
ggplot(iris_cl) +
geom_point(aes(x = sepal_length, y = sepal_width, color = cluster_no))
ggplot(iris_cl) +
geom_point(aes(x= petal_length, y = petal_width, color = cluster_no, pch = species)) +
scale_color_brewer(palette = "Set2")
# Add plotly for 3D
plot_ly(x = iris_cl$petal_length,
y = iris_cl$petal_width,
z = iris_cl$sepal_width,
type = "scatter3d",
color = iris_cl$cluster_no,
symbol = iris_cl$species,
colors = "Set1")
## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode